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This operator uses a kalman filter to estimate the distribution of one or more attribute values.

Parameter

• attributesAttributes: The attributes to perform the filter on
• Transition: The transition matrix
• ProcessNoise: The transition process noise matrix
• controlMeasurement: The control measurement matrix
• processnoiseMeasurementNoise: The process measurement noise matrixmeasurement
• InitialState: The initial state vector (optional)
• InitialError: The measurement initial error matrix (optional)measurementnoise
• Control: The measurement noise control matrix (optional)

Example

out = KALMAN({MEASUREMENT = '[1.0]', TRANSITION = '[1.0]', ProcessNoise = '[2.0]', ATTRIBUTES = ['x'], MEASUREMENTNOISE = '[4.0]'}, in)

outputin = kalmanfilterKALMAN({attributesINITIALSTATE = '['x','y'], transition=[], control=[], processnoise=[], measurement=[], measurementnoies=[]}, input0.0, 0.0, 0.0, 0.0]', INITIALERROR = '[1.0,0.0,0.0,0.0;0.0,1.0,0.0,0.0;0.0,0.0,1.0,0.0;0.0,0.0,0.0,1.0]', MEASUREMENT = '[0.0,0.0,1.0,0.0;0.0,0.0,0.0,1.0]', TRANSITION = '[1.0,0.0,1.0,0.0;0.0,1.0,0.0,1.0;0.0,0.0,1.0,0.0;0.0,0.0,0.0,1.0]', PROCESSNOISE = '[1/4, 1/4, 1/2, 1/2;1/4, 1/4, 1/2, 1/2; 1/2, 1/2, 1, 1; 1/2, 1/2, 1, 1]', ATTRIBUTES = ['x1','x2'], MEASUREMENTNOISE = '[10.0,0.0;0.0,10.0]'}, out)